Triple
T3477701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rhododendron |
E73413
|
entity |
| Predicate | toxicPart |
P48985
|
FINISHED |
| Object | leaves |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: leaves | Statement: [Rhododendron, toxicPart, leaves]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toxicPart Context triple: [Rhododendron, toxicPart, leaves]
-
A.
toxicTo
Indicates that one entity causes harm, poisoning, or adverse effects to another when exposed or applied.
-
B.
toxinType
Indicates the specific kind or category of toxin associated with an entity.
-
C.
venomous
Indicates that an organism possesses venom and can inject or deliver it to another organism, typically as a means of defense or predation.
-
D.
toxinProduced
Indicates that one entity generates or secretes a substance that is toxic or harmful to another entity or its environment.
-
E.
victimParticipation
Indicates that the victim took part in, contributed to, or was otherwise involved in the event or action described.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb5b7584819096eadd1c5268eab1 |
completed | March 8, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69adae0935ac8190bfa8a8bd3dcd3301 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb1ecb02881908394f197e31431b4 |
completed | March 8, 2026, 5:29 p.m. |
Created at: March 8, 2026, 3:17 p.m.